When Quality Analysis Meets AI: A Lifesaving Skill for the Automotive Industry
Recentlywhile browsing GitHubI came across a rather interesting project called Geely Quality Rootcause CopilotTo be honestas a programmer with a bit of an interest in industrial manufacturingseeing a tool that combines traditional quality management methods with AI immediately made me think"Wowthis thing could really be a huge help."
So what exactly does this skill do? In simple termsit provides root cause analysis support specifically for quality defects that occur during the automotive manufacturing processAs everyone knowsautomotive production lines involve numerous parts and complex processesOnce a defect appearsif the root cause isn't identified in timethe consequences can range from rework on the lighter end to affecting an entire batch or even triggering a recall on the heavier endWhile the traditional 8D and QRQC methods are quite maturethey are extremely tedious to execute—especially when it comes to writing those reportsdrawing fishbone diagramsand prioritizing actionsIt can really give you a headache.
This Copilot is designed to solve exactly that pain pointIt automates the entire 8D and QRQC processAll you need to do is feed it data such as defect symptomspart namesproduction line informationand defect countsand it will help you generate a D2 problem statementThenit builds fishbone diagram hypotheses based on the 4M framework (ManMachineMaterialMethod)ranks possible root causes according to data confidence levelsand finally proposes containment actions and verification stepsBy the end of the entire processa draft 8D report is readycomplete with assigned owners and deadlines filled in for you.
Installation and Usage: It's Really Not That Complicated
Installing this skill is actually very simple—it's pretty much like installing an npm packageThe officially recommended way is to use the npx command:
npx skills add https://github.com/ZiyaoMou/industrial-skilled-agent --skill geely-quality-rootcause-copilot
The prerequisite is that you have Node.js installedand then you just let npx handle the restAfter installationyou can directly invoke it in any AI workbench that supports skillsI tried it out myselfand it runs particularly smoothly in You simply input the relevant informationand within secondsyou get the analysis results.
When using ityou need to prepare three types of input:
- Basic defect description: For examplewhich part is affected and what the symptom is (scratchesdimensional deviationetc.)
- Production environment information: Production line numberstation numbershiftetc.
- Data statistics: Defect counts tallied by batchsupplierand process parameters
The more detailed this information isthe more reliable the analysis results will beIt's just like going to see a doctor—the more clearly you describe your symptomsthe more accurate the diagnosis will be.
Hands-On Experience: Time-SavingEffort-Reducingand Reliable
I tested it out using a simulated caseLet's say a porosity defect appeared in an engine blockI input the part nameproduction line informationand defect rate dataIt quickly generated a D2 problem statement: "Porosity defects occurred in the engine block casting processwith a defect rate of 0.8%exceeding the control limit." Thenthe fishbone diagram listed possible causes across the four 4M dimensionssuch as insufficient operator training (Man)unstable mold temperature (Machine)high gas content in the molten aluminum (Material)and excessively fast pouring speed (Method)Finallybased on data confidence rankingit placed "high gas content in molten aluminum" at the top of the list and suggested containment actions—suspending shipment of that batch and increasing the frequency of furnace-side inspections.
The entire process took just a few minutesIn the pastjust drawing the fishbone diagram alone would have taken half a dayMoreoverthe 8D report draft it outputs is directly in markdown formatso you can copy it into a documentpolish it up slightlyand it's ready to useIt's incredibly convenient.
Howeverone thing to keep in mind is that this skill is better suited as an auxiliary toolThe hypotheses and measures it provides need to be verified by quality engineers in conjunction with the actual situationAfter allno matter how smart AI getsit cannot completely replace human experience and judgment.
Applicable Scenarios and a Few Small Suggestions
If you work in quality-related roles in the automotive manufacturing industry or similar discrete manufacturing sectorsthis skill is definitely worth tryingWhether it's for daily defect handling or periodic quality improvement projectsit can help boost your efficiencyEspecially for quality engineers who are new to the fieldit can also provide analytical thinking frameworks—essentially acting like a portable mentor.
One final reminder: before using itit's best to familiarize yourself with the basic concepts of 8D and QRQCThis wayyou'll be better equipped to understand the content it outputs and utilize it more effectivelyAlrightthat's all for my sharingIf you're interestedgo ahead and give it a try!
